Temporal convolutional denoising autoencoder network for air pollution prediction with missing values

In recent years, people are paying more attention to improve air quality levels to mitigate its negative impact on human health. So, effective air pollution control has become one of the hottest environmental issues. Accurate pollution prediction plays a vital role in air pollution control. However,...

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Bibliographic Details
Published in:Urban climate Vol. 38; p. 100872
Main Authors: Samal, K. Krishna Rani, Babu, Korra Sathya, Das, Santos Kumar
Format: Journal Article
Language:English
Published: Elsevier B.V 01.07.2021
Subjects:
Autoencoder
LSTM
PM2.5
Temporal convolutional network
Time series prediction
Time series prediction
LSTM
Temporal convolutional network
Autoencoder
PM2.5
ISSN:2212-0955, 2212-0955
Online Access:Get full text
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Summary:In recent years, people are paying more attention to improve air quality levels to mitigate its negative impact on human health. So, effective air pollution control has become one of the hottest environmental issues. Accurate pollution prediction plays a vital role in air pollution control. However, air quality modeling faces challenges like long-term pollutant variations due to meteorological variables impact and missing values due to natural disaster or sensor shutdown. These issues make air quality models more complex and challenging to introspect. So, we developed Temporal Convolutional Denoising Autoencoder (TCDA) network, a hybrid PM2.5 prediction framework that can perform rapid extraction of complex dataset's features, handle missing values and improve PM2.5 prediction results. This research paper includes Temporal Convolutional Network (TCN) and Denoising Autoencoder (DAE) network to handle nonlinear multivariate massive datasets. The former utilizes parallel feature processing of Convolutional Neural Network (CNN) and temporal component modeling ability of Recurrent Neural Network (RNN), which helps for features extraction from complex dataset. The latter is to reconstruct the error to fine-tune the prediction results and handle missing values. The proposed model's prediction capability is evaluated by comparing its performance with other baseline models, which shows its performance superiority over other models. •Handling missing values has a significant role in improving pollution prediction results.•Data imputation and forecasting are two major steps towards proper air pollution management.•Temporal Convolutional Denoising Autoencoder can handle different patterns of missing values to predict PM2.5 concentration.
ISSN:2212-0955
2212-0955
DOI:10.1016/j.uclim.2021.100872